Relative merits of different types of
multi-wavelength observations to
constrain galaxy physical parameters
Camilla Pacifici - Institut d’Astrophysique de Paris
Supervisor: Stephane Charlot
Madrid, ELIXIR annual meeting, 5-6 / 10 / 2011
Outline
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Motivation
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Current and future applications (e.g. UV emission of
high-redshift galaxies)
Modeling approach
Example of constraints on galaxy physical parameters
Assess relative merits of different types of
observations to constrain some main parameters
Motivation
characterize physical properties of galaxies from their light
• observations at different cosmic epochs
• using different types of multi-wavelength observations
• stellar mass
• SF history
• metallicity
• .....
quantify the accuracy to which physical
parameters can be extracted from various
photometric and spectroscopic observations
Modeling
required to relate observables to physical parameters
build library of galaxy spectra which reproduce
a wide range of observables
UV properties of highredshift galaxies (HST)
Bouwens et al. (2011)
Brinchmann et al. (2004)
optical properties of
local galaxies (SDSS)
Modeling
required to relate observables to physical parameters
build library of galaxy spectra which reproduce
a wide range of observables
Appeal to state-of-the-art models to include:
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physically motivated SF and chemical enrichment histories
(from simulations)
latest progress in the spectral modeling of stellar populations
contamination of stellar emission by nebular emission
recent prescriptions for attenuation by dust
(comprehensive range of parameters to account for models uncertainties)
State-of-the-art modeling
A
B
C
SF and
chemical
enrichment
histories
• stellar emission
• nebular emission
• dust attenuation
galaxy
spectra
used to estimate physical parameters from
multi-wavelength observations
Library of SF and chemical enrichment histories
semi-analytic postprocessing of the
Millennium Simulation
Springel et al. (2005)
De Lucia & Blaizot (2007)
redshift of observation
draw 5,000,000
model galaxies in
wide ranges of
evolutionary stages
A
Galaxy spectral modeling
1. stellar component
2. nebular emission
3. dust attenuation
Galaxev code (Charlot & Bruzual in preparation)
TP-AGB stars
high-resolution rest-frame UV
B
Galaxy spectral modeling
1. stellar component
2. nebular emission Galaxev + CLOUDY (Charlot & Longhetti 2001)
3. dust attenuation
B
Galaxy spectral modeling
1. stellar component
2. nebular emission
3. dust attenuation 2-component model (Charlot & Fall 2000)
include uncertainties in:
• optical properties
• spatial distribution
• galaxy orientation (Chevallard et al. in preparation)
by considering ranges of:
• relative proportions of dust in giant
molecular clouds and diffuse ISM
• slopes of the attenuation curve in diffuse ISM
B
Galaxy spectral modeling
use these models
to generate SEDs
of 5 millions
galaxies in the
library
C
Estimates of physical parameters
Mimic any type of
observation by
convolving model
SED with instrument
responses
• broad-band photometry
• emission-line luminosity
• emission-line equivalent width
• absorption features
• entire spectrum at different resolutions
estimate physical
parameters of
observed galaxies by
comparison with
every model in
library (Bayesian)
Originality: stellar
continuum and
nebular emission
fitted simultaneously
Example of parameter estimates
Elisabete da Cunha (MPIA), Hans-Walter Rix (MPIA), 3D-HST data
Accuracy in these estimates
Pacifici et al. (submitted)
goal: assess accuracy and
uncertainty to which physical
parameters can be estimated
physical parameters of
observed galaxies cannot
be known a priori
accuracy
uncertainty
require model
galaxies with known
parameters
simulate observations by adding
noise to state-of-the-art models
(assume models are good approximations of true galaxies)
Example of parameter retrieval
SPECTRAL FIT
rest-frame optical spectrum
S/N~20, R=100
• mass-to-light ratio
• fraction of stellar mass
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formed in the last 2.5 Gyr
specific SFR
gas-phase oxygen abundance
dust attenuation optical
depth
fraction dust in the ISM
Parameter retrieval from
different types of observations
broad-band photometry ugriz
S/N=30
5,000,000 models, 10,000 pseudo-observation
16% - 84% confidence interval
ugriz
Parameter retrieval from
different types of observations
spectral fit low-resolution (R=100, FWHM=50 Å)
S/N=20
5,000,000 models, 10,000 pseudo-observation
16% - 84% confidence interval
R=100
Parameter retrieval from
different types of observations
spectral fit medium-resolution (R=1000, FWHM=5 Å)
S/N=20
5,000,000 models, 10,000 pseudo-observation
16% - 84% confidence interval
R=1000
Up to now...
• new approach to constrain galaxy physical parameters from
combined interpretation of stellar and nebular emission
• assessed relative merits of different types of optical observations
• presented examples of this application
• approach applicable to the analysis of any type of observation
(including combination of photometric and spectroscopic data)
across the wavelength range covered by spectral evolution
models (e.g., 3D-HST survey)
.........
Next: rest-frame UV emission
approach extendable to the analysis of any
observable within reach of the models
explore rest-frame UV in high-redshift galaxies
Appeal to:
• refined library of SF histories for z > 2 galaxies
• new, high-resolution UV stellar spectra (+ nebular emission)
Specific questions include:
• accuracy of photometric redshift determinations for different S/N
• UV slope (dependence on dust and other model parameters)
• age and metallicity from (UV) absorption-line features
• ...